ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting
counterfactual statements and detecting antecedent and consequence. This paper
describes our system which is based on pre-trained transformers. For the first
subtask, we train several transformer-based classifiers for detecting
counterfactual statements. For the second subtask, we formulate antecedent and
consequence extraction as a query-based question answering problem. The two
subsystems both achieved third place in the evaluation. Our system is openly
released at this https URL

该论文介绍了 ISCAS 的系统在 SemEval 2020 任务 5 的两个子任务中的表现，基于预先训练的 transformers，第一个子任务使用基于 transformers 的分类器来检测反事实陈述，第二个子任务将先决条件和后果提取为一个基于查询的问答问题，两个子系统在评估中都获得了第三名。我们的系统是公开发布的。

ISCAS 在 SemEval-2020 任务 5 中：针对反事实语句建模的预训练 Transformer

ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for  Counterfactual Statement Modeling

Recent work has shown that some common machine learning classifiers can be
compiled into Boolean circuits that have the same input-output behavior. We
present a theory for unveiling the reasons behind the decisions made by Boolean
classifiers and study some of its theoretical and practical implications. We
define notions such as sufficient, necessary and complete reasons behind
decisions, in addition to classifier and decision bias. We show how these
notions can be used to evaluate counterfactual statements such as "a decision
will stick even if ... because ... ." We present efficient algorithms for
computing these notions, which are based on new advances on tractable Boolean
circuits, and illustrate them using a case study.

本文探讨机器学习分类器编译成布尔电路同样具有输入 - 输出行为，定义二进制分类器决策背后的决策原因，并阐述其在应用层面的影响。此外，还提出了 “充分的”，“必要的” 和 “完整的” 决策原因的概念，以及对分类器和决策偏差的定义。根据这些概念，我们提出了高效的算法，并使用案例研究来论证它们的实用性。